DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Donghyeon | ko |
dc.contributor.author | Lee, Jinsu | ko |
dc.contributor.author | Lee, Jinmook | ko |
dc.contributor.author | Yoo, Hoi-Jun | ko |
dc.date.accessioned | 2019-11-29T05:23:46Z | - |
dc.date.available | 2019-11-29T05:23:46Z | - |
dc.date.created | 2019-11-27 | - |
dc.date.created | 2019-11-27 | - |
dc.date.issued | 2019-06 | - |
dc.identifier.citation | 33rd Symposium on VLSI Circuits, VLSI Circuits 2019, pp.C304 - C305 | - |
dc.identifier.uri | http://hdl.handle.net/10203/268706 | - |
dc.description.abstract | An energy efficient deep neural network (DNN) learning processor is proposed using direct feedback alignment (DFA). The proposed processor achieves 2.2 × faster learning speed compared with the previous learning processors by the pipelined DFA (PDFA). In order to enhance the energy efficiency by 38.7%, the heterogeneous learning core (LC) architecture is optimized with the 11-stage pipeline data-path. Furthermore, direct error propagation core (DEPC) utilizes random number generators (RNG) to remove external memory access (EMA) caused by error propagation (EP) and improve the energy efficiency by 19.9%. The proposed PDFA based learning processor is evaluated on the object tracking (OT) application, and as a result, it shows 34.4 frames-per-second (FPS) throughput with 1.32 TOPS/W energy efficiency. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture | - |
dc.type | Conference | - |
dc.identifier.wosid | 000531736500104 | - |
dc.identifier.scopusid | 2-s2.0-85073896865 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | C304 | - |
dc.citation.endingpage | C305 | - |
dc.citation.publicationname | 33rd Symposium on VLSI Circuits, VLSI Circuits 2019 | - |
dc.identifier.conferencecountry | JA | - |
dc.identifier.conferencelocation | Kyoto | - |
dc.identifier.doi | 10.23919/VLSIC.2019.8778006 | - |
dc.contributor.localauthor | Yoo, Hoi-Jun | - |
dc.contributor.nonIdAuthor | Han, Donghyeon | - |
dc.contributor.nonIdAuthor | Lee, Jinsu | - |
dc.contributor.nonIdAuthor | Lee, Jinmook | - |
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